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Contents

1 一危
2 殊 覲
3 蟆郁骸伎
4 iris
5 谿瑚襭


1 一危 #

[http]EXCEL 譟一覦覯 覦 糾覿襯 伎.
cname <- c("ID", "蟲襷る", "磯","碁一", "碁", "覦覓碁", "蟇一朱")
x = read.table("c:\\data\\disc.txt", col.names = cname)
head(x)
disc.txt

> head(x)
  ID  蟲襷る 磯  碁一  碁  覦覓碁  蟇一朱
1  1          A   48       9000          4        5        6
2  2          A   58       8000          6        4       20
3  3          A   52       7000          6        4       12
4  4          A   63       7000          6        4       15
5  5          A   59       8000          4        6        6
6  6          A   38      11000          5        4       10
> 

蟲襷る A, B覲襦 蠏(mean)螻 覿(var) 螻 苦朱 螳螳 觚 襷 螻壱.
x1 <- subset(x, 蟲襷る=="A")
x2 <- subset(x, 蟲襷る=="B")

2 殊 覲 #

library("MASS")
rs<-lda(蟲襷る~磯+碁一+碁+覦覓碁+蟇一朱, data=x)
rs

蟆郁骸
> rs
Call:
lda(蟲襷る ~ 磯 + 碁一 + 碁 + 覦覓碁 + 
    蟇一朱, data = x)

Prior probabilities of groups:
  A   B 
0.5 0.5 

Group means:
  磯  碁一  碁  覦覓碁  蟇一朱
A 55.0       8400        4.8      4.6      9.3
B 32.9       5400        2.3      1.9      2.5

Coefficients of linear discriminants:
                     LD1
磯       -0.0982250865
碁一 -0.0008926856
碁 -0.7287341389
覦覓碁   -0.6346846738
蟇一朱   -0.0578973013
> 

3 蟆郁骸伎 #

p<-predict(rs)
x <- cbind(x, p$x)
x

蟆郁骸
> x
   ID  蟲襷る  磯 碁一  碁  覦覓碁  蟇一朱       LD1
1   1          A   48       9000          4        5        6 -3.716870
2   2          A   58       8000          6        4       20 -5.439781
3   3          A   52       7000          6        4       12 -3.494566
4   4          A   63       7000          6        4       15 -4.748734
5   5          A   59       8000          4        6        6 -4.539345
6   6          A   38      11000          5        4       10 -4.845629
7   7          A   49       9000          5        5        9 -4.717521
8   8          A   62       8000          3        7        5 -4.682073
9   9          A   55       9000          5        3        5 -3.805913
10 10          A   66       8000          4        4        5 -3.899654
11 11          B   28       6000          2        2        2  4.518800
12 12          B   33       4000          3        3        3  4.391730
13 13          B   26       5000          3        1        2  5.513887
14 14          B   40       7000          2        2        1  2.505311
15 15          B   31       5000          2        1        3  5.693598
16 16          B   24       6000          2        1        3  5.488488
17 17          B   47       6000          3        1        1  2.616372
18 18          B   30       7000          1        2        3  4.100501
19 19          B   40       4000          1        2        2  5.854205
20 20          B   30       4000          4        4        5  3.207192
> 

LD1螳 A觚襯 蟲襷ろ 願, B觚襯 蟲襷ろ . 覲り 覲企 . れ ろ企慨覃 螻磯 蟆郁骸螳 15.46785襯 蠍一朱 る 蟆 .

calc <- with(x, 磯 * -0.0982250865 + 碁一 * -0.0008926856 + 碁 * -0.7287341389 + 覦覓碁 * -0.6346846738 + 蟇一朱 * -0.0578973013)
x <- cbind(x, calc)
calc2 <- with(x, LD1 - calc)
x <- cbind(x, calc2)
x

蟆郁骸
> x
   ID 蟲襷る 磯 碁一 碁 覦覓碁 蟇一朱       LD1       calc    calc2
1   1          A   48       9000          4        5        6 -3.716870 -19.184718 15.46785
2   2          A   58       8000          6        4       20 -5.439781 -20.907629 15.46785
3   3          A   52       7000          6        4       12 -3.494566 -18.962415 15.46785
4   4          A   63       7000          6        4       15 -4.748734 -20.216583 15.46785
5   5          A   59       8000          4        6        6 -4.539345 -20.007193 15.46785
6   6          A   38      11000          5        4       10 -4.845629 -20.313477 15.46785
7   7          A   49       9000          5        5        9 -4.717521 -20.185369 15.46785
8   8          A   62       8000          3        7        5 -4.682073 -20.149922 15.46785
9   9          A   55       9000          5        3        5 -3.805913 -19.273761 15.46785
10 10          A   66       8000          4        4        5 -3.899654 -19.367502 15.46785
11 11          B   28       6000          2        2        2  4.518800 -10.949048 15.46785
12 12          B   33       4000          3        3        3  4.391730 -11.076119 15.46785
13 13          B   26       5000          3        1        2  5.513887  -9.953962 15.46785
14 14          B   40       7000          2        2        1  2.505311 -12.962538 15.46785
15 15          B   31       5000          2        1        3  5.693598  -9.774251 15.46785
16 16          B   24       6000          2        1        3  5.488488  -9.979361 15.46785
17 17          B   47       6000          3        1        1  2.616372 -12.851477 15.46785
18 18          B   30       7000          1        2        3  4.100501 -11.367347 15.46785
19 19          B   40       4000          1        2        2  5.854205  -9.613644 15.46785
20 20          B   30       4000          4        4        5  3.207192 -12.260657 15.46785

碁襦 覲 .
cname <- c("ID", "蟲襷る", "磯","碁一", "碁", "覦覓碁", "蟇一朱")
x = read.table("c:\\data\\disc.txt", col.names = cname)
rs<-lda(碁~磯+碁一+蟲襷る+覦覓碁+蟇一朱, data=x)
rs
p<-predict(rs)
x <- cbind(x, p$x)
x

蟆郁骸
> rs
Call:
lda(碁 ~ 磯 + 碁一 + 蟲襷る + 覦覓碁 + 
    蟇一朱, data = x)

Prior probabilities of groups:
   1    2    3    4    5    6 
0.10 0.20 0.20 0.20 0.15 0.15 

Group means:
      磯 碁一 蟲襷るB 覦覓碁 蟇一朱
1 35.00000   5500.000        1.00     2.00  2.50000
2 30.75000   6000.000        1.00     1.50  2.25000
3 42.00000   5750.000        0.75     3.00  2.75000
4 50.75000   7250.000        0.25     4.75  5.50000
5 47.33333   9666.667        0.00     4.00  8.00000
6 57.66667   7333.333        0.00     4.00 15.66667

Coefficients of linear discriminants:
                      LD1           LD2           LD3          LD4
磯         0.0108341882 -0.0842979470 -0.0182453017 0.0079837572
碁一  -0.0005159674  0.0003233735  0.0005008073 0.0005652328
蟲襷るB -3.5186494778 -3.6997526685  1.3377932310 5.0233382564
覦覓碁    -0.5541865385 -0.0619435929 -0.5560926546 0.7505362060
蟇一朱     0.5078180145 -0.1710574267  0.1361173174 0.1627906244
                      LD5
磯        -0.1117330322
碁一  -0.0005554468
蟲襷るB -3.7199816354
覦覓碁     0.0469918970
蟇一朱    -0.0118410355

Proportion of trace:
   LD1    LD2    LD3    LD4    LD5 
0.8430 0.0983 0.0578 0.0006 0.0004 

> x
   ID  蟲襷る  磯  碁一 碁  覦覓碁  蟇一朱        LD1         LD2        LD3         LD4        LD5
1   1          A   48       9000          4        5        6 -0.1993729  2.06204706 -0.6506451  0.03737132  0.3220854
2   2          A   58       8000          6        4       20  8.0885751 -1.43716633  1.1278296  1.08050866 -0.4525645
3   3          A   52       7000          6        4       12  4.4769932  0.11370722 -0.3524444 -0.83495164  0.8680088
4   4          A   63       7000          6        4       15  6.1196233 -1.32674248 -0.1447908 -0.25875844 -0.3965776
5   5          A   59       8000          4        6        6 -0.1184160  0.74945251 -1.9082434  0.31049609 -0.3045392
6   6          A   38      11000          5        4       10  1.2458091  2.92948751  1.6339845  0.98862558  0.2341661
7   7          A   49       9000          5        5        9  1.3349153  1.46457684 -0.2605384  0.53372695  0.1748293
8   8          A   62       8000          3        7        5 -1.1479180  0.60567250 -2.6551893  0.92219294 -0.5809054
9   9          A   55       9000          5        3        5  0.4770215  1.76690605  0.1977058 -1.57060541 -0.5421886
10 10          A   66       8000          4        4        5  0.5579784  0.45431149 -1.0598925 -1.29748065 -1.1688132
11 11          B   28       6000          2        2        2 -2.7555165 -0.05180681  0.6734409  0.30256502  0.4094934
12 12          B   33       4000          3        3        3 -1.7157794 -1.35304466 -0.8393757  0.12534510  0.9968727
13 13          B   26       5000          3        1        2 -1.7070310 -0.14464087  0.7652168 -1.02917147  1.1414143
14 14          B   40       7000          2        2        1 -3.6492916 -0.56895121  0.8191873  0.80081225 -1.4749088
15 15          B   31       5000          2        1        3 -1.1450420 -0.73718803  0.8101076 -0.82646206  0.5709081
16 16          B   24       6000          2        1        3 -1.7368487  0.17627114  1.4386320 -0.31711559  0.7975926
17 17          B   47       6000          3        1        1 -2.5032984 -1.42046679  0.7467555 -0.45907043 -1.7485851
18 18          B   30       7000          1        2        3 -2.7419975 -0.06808659  1.2738749  1.04655592 -0.3812606
19 19          B   40       4000          1        2        2 -1.5935715 -1.71012927 -0.5471174 -0.73209543  0.1795906
20 20          B   30       4000          4        4        5 -1.2868324 -1.50420926 -1.0684978  1.17751128  1.3553816

4 iris #

tmp <- iris
library("MASS")
rs<-lda(Species~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width, data=tmp)
rs

蟆郁骸
> rs
Call:
lda(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, 
    data = tmp)

Prior probabilities of groups:
    setosa versicolor  virginica 
 0.3333333  0.3333333  0.3333333 

Group means:
           Sepal.Length Sepal.Width Petal.Length Petal.Width
setosa            5.006       3.428        1.462       0.246
versicolor        5.936       2.770        4.260       1.326
virginica         6.588       2.974        5.552       2.026

Coefficients of linear discriminants:
                    LD1         LD2
Sepal.Length  0.8293776  0.02410215
Sepal.Width   1.5344731  2.16452123
Petal.Length -2.2012117 -0.93192121
Petal.Width  -2.8104603  2.83918785

Proportion of trace:
   LD1    LD2 
0.9912 0.0088 
> 
Proportion of trace螳 LD1 0.9912. LD1襷 蟆.

p <- predict(rs)
tmp <- cbind(tmp, p$x)
tmp[5:7]

plot(rs)
plot(rs, dimen=1, type="both") 

lda01.png
x豢 LD1願, y豢 LD2. 覘豎 覿覿 螳朱 .

lda02.png
ろ蠏碁殊朱 蠏碁9覲襦 一危一 覿螳 企讌 .

mean(tmp$LD1[tmp$Species == "setosa"])
mean(tmp$LD1[tmp$Species == "versicolor"])
mean(tmp$LD1[tmp$Species == "virginica"])

蟆郁骸
> mean(tmp$LD1[tmp$Species == "setosa"])
[1] 7.6076
> mean(tmp$LD1[tmp$Species == "versicolor"])
[1] -1.825049
> mean(tmp$LD1[tmp$Species == "virginica"])
[1] -5.78255


蠍 蠍郁鍵..
企: : るジ讓曙 襦螻豺 企Ν 譯殊語. 襦螻豺
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